The present invention is directed to a method and system for detection and delineation of abnormal regions in volumetric image sets and is more particularly directed to such a method and system in which the detection and delineation are automated through the use of spectral and spatial information.
Numerous disease processes are characterized by the development of lesions that are structurally and compositionally distinct from surrounding healthy tissue. Some examples include multiple sclerosis and Alzheimer""s disease, which are characterized by the development of brain lesions or plaques, and the sub-set of cancers that include the development of solid tumors in the brain, bones, or organs. In order to assess the progress or response to treatment of these diseases, it is necessary to obtain some measure of the patient""s total lesion burden. In some cases, it is also helpful to know specific things about the structure of individual lesions.
Clearly, any accurate measure of a complex three-dimensional structure requires a three-dimensional image set. For this reason, magnetic resonance imaging (MRI) and computed tomography (CT), which provide complete volume imagery, are preferred over plain films for these assessments. Once the imagery has been obtained, however, any assessment requires the location, identification, and delineation of all lesions within the volume. Current standard practice requires an expert, typically a radiologist, to read each of the 30-100 images in the volume set, identify any lesions present, and trace out the boundaries of each lesion using specialized computer software. The traced boundaries are then used to calculate lesion volumes and other biomarkers.
This procedure has a number of obvious drawbacks. First, it is both tedious and time consuming. Manual tracing of a single volume data set can take anywhere from 15 minutes to two hours or more, depending on the number of images in the set and the number of lesions per image. Second, because manual outlining is heavily dependent on the opinion of the observer, it produces results that are subject to both error and bias. Recent studies have shown coefficients of variation of 5% or more for repeated tracings of the same structures by a single observer, and of up to 50% in some cases for tracings of structures by multiple observers. Such wide variability renders the results of such an analysis nearly useless, and points out a clear need for an improved, preferably automated, method of measurement. Some examples of prior work in this field include:
[1] E. Ashton et al., xe2x80x9cAutomated Measurement of Structures in CT and MR Imagery: A Validation Studyxe2x80x9d Proc. of IEEE-Computer Based Medical Systems, pp. 300-305 (2001).
[2] R. Chung, C. Ho, xe2x80x9c3-D Reconstruction from tomographic data using 2-D active contoursxe2x80x9d Computers and Biomedical Research (33), pp. 186-210 (2000).
[3] K. Juottonen et al. xe2x80x9cVolumes of the entorhinal and perirhinal cortices in Alzheimer""s diseasexe2x80x9d Neurobiology of Aging (19), pp.15-22 (1998).
[4] E. Ashton, K. Parker, M. Berg, C. Chen, xe2x80x9cA Novel Volumetric Feature Extraction Technique with Applications to MR Imagesxe2x80x9d IEEE Trans. Medical Imaging (16), pp.365-371 (1997).
[5] E. Ashton et al., xe2x80x9cSegmentation and Feature Extraction Techniques, with Applications to MRI Head Studiesxe2x80x9d Magnetic Resonance in Medicine (33), pp. 670-677 (1995).
[6] D. Taylor, W. Barrett, xe2x80x9cImage segmentation using globally optimum growth in three dimensions with an adaptive feature setxe2x80x9d Visualization in Biomedical Computing, pp. 98-107 (1994).
[7] I. Carlbom, D. Terzopoulos, K. Harris, xe2x80x9cComputer assisted registration, segmentation, and 3-D reconstruction from images of neuronal tissue sectionsxe2x80x9d IEEE Trans. Medical Imaging(13), pp. 351-362 (1994).
[8] F. Cendes et al., xe2x80x9cMRI volumetric measurement of amygdala and hippocampus in temporal lobe epilepsyxe2x80x9d Neurology (43), pp. 719-725 (1993).
All of the above referenced work describes schemes that require a human observer to identify the location of each lesion in the volume manually. Most also require some operator input regarding lesion shape and size. These limitations reduce the precision advantage provided over pure manual tracing by introducing subjective opinion into the identification process, and reduce the speed advantage by requiring extensive operator input. Prior work in the area of automated detection of abnormal regions in imagery using grayscale or spectral information includes:
[9] Z. Ge, V. Venkatesan, S. Mitra, xe2x80x9cA Statistical 3-D Segmentation Algorithm for Classifying Brain Tissues in Multiple Sclerosisxe2x80x9d Proc. of IEEE-Computer Based Medical Systems, pp. 455-460 (2001).
[10] K. Van Leemput et al., xe2x80x9cAutomated Segmentation of Multiple Sclerosis Lesions by Model Outlier Detectionxe2x80x9d IEEE Trans. Medical Imaging (20), pp. 677-688 (2001).
[11] E. Ashton, xe2x80x9cMultialgorithm solution for automated multispectral target detectionxe2x80x9d Optical Engineering (38), pp. 717-724 (1999).
[12] E. Ashton, xe2x80x9cDetection of sub-pixel anomalies in multspectral infrared imagery using an adaptive Bayesian classifierxe2x80x9d IEEE Trans. Geoscience and Remote Sensing (36), pp. 506-517 (1998).
[13] E. Ashton, A. Schaum, xe2x80x9cAlgorithms for the Detection of Sub-Pixel Targets in Multispectral Imageryxe2x80x9d Photogrammetric Engineering and Remote Sensing (64), pp. 723-731 (1998).
[14] R. Muise, xe2x80x9cCoastal mine detection using the COBRA multispectral sensorxe2x80x9d SPIE Detection Remediation Tech. Mines Minelike Targets (2765), pp. 15-24 (1996).
[15] T. Watanabe et al., xe2x80x9cImproved contextual classifiers of multispectral image dataxe2x80x9d IEICE Trans. Fundamentals Elect. Commun., Comput. Sci,(E77-A), pp. 1445-1450 (1994).
[16] X. Yu, I. Reed, A. Stocker, xe2x80x9cComparative performance analysis of adaptive multi-spectral detectorsxe2x80x9d IEEE Trans. Signal Processing (41), pp. 2639-2656 (1993).
[17] I. Reed, X. Yu, xe2x80x9cAdaptive multiple-band CFAR detection of an optical pattern with unknown spectral distributionxe2x80x9d IEEE Trans. Acoustics, Speech, Signal Processing (38), pp. 1760-1770 (1990).
The systems described in these references have very similar theoretical bases and suffer from two common limitations. First, they make primary use only of either spatial/grayscale information (9,10) or spectral signature (11-17). Second, all of these systems operate by forming a statistical model of common background tissues and then searching for outliers. The resulting lack of a priori target information causes these systems to be non-specific and to have impractically high false alarm rates.
It will be readily apparent from the above that a need exists in the art to overcome the above-noted problems caused by existing techniques for identification and delineation of lesion boundaries. It is therefore an object of the invention to detect and delineate abnormal structures with higher accuracy.
It is another object of the invention to allow increased speed in the detection and delineation of abnormal structures.
It is yet another object of the invention to remove human error from the detection and delineation of abnormal structures.
It is still another object of the invention to reduce the rate of false alarms.
To achieve the above and other objects, the present invention makes use of spectral and spatial information to provide automated detection and delineation of abnormal structures. The present invention goes beyond and improves the work described in these references in two ways. First, it uses statistical techniques which permit the use of significant spatial and spectral information. Second, it allows for a directed search for a particular grayscale or spectral anomaly, presented, e.g., as a user-defined exemplar, whereas the prior work focuses on background characterization and generalized anomaly detection. This allows the system described in this work to be both sensitive and specific, providing a high probability of detection coupled with a low false alarm rate.
The present invention provides a system and method for detection and delineation of abnormal regions in volumetric image sets through five basic steps:
(1) Noise suppression in the original data through digital filtering, including either low-pass or miedian filtering.
(2) Background characterization.
(3) Identification of an exemplar.
(4) Identification of statistically similar structures throughout the volume. A technique for doing so, called directed clustering, will be disclosed, although any suitable decision metric can be used.
(5) Extraction of quantitative information (lesion volume, shape, etc.) and output, e.g., to a database.
While various techniques which by themselves are known in the prior art can be incorporated into the present invention, their use in the context of the present invention is considered to be novel.